phytorch-lib 0.1.0

PhyTorch is a comprehensive PyTorch-based toolkit for modeling plant physiological processes including photosynthesis, stomatal conductance, leaf hydraulics, and optical properties.

PhyTorch

A Comprehensive Physiological Plant Modeling Toolkit

PhyTorch is a PyTorch-based package for modeling plant physiological processes. It provides efficient, GPU-accelerated implementations of models for photosynthesis, stomatal conductance, leaf hydraulics, and optical properties.

Features

• Photosynthesis Models: FvCB (Farquhar-von Caemmerer-Berry) model with flexible temperature and light response functions
• Stomatal Conductance: Multiple empirical and semi-empirical models (Medlyn, Ball-Woodrow-Berry, Buckley-Mott-Farquhar)
• Leaf Optical Properties: PROSPECT model for leaf spectral reflectance and transmittance
• Leaf Hydraulics: Coming soon - Models for leaf water transport and hydraulic conductance
• GPU Acceleration: Leverage PyTorch for fast parameter optimization on CPU or GPU
• Automatic Differentiation: Efficient gradient-based optimization for model fitting

Installation

From PyPI (recommended)

pip install phytorch

From source

git clone https://github.com/PlantSimulationLab/phytorch.git
cd phytorch
pip install -e .

Quick Start

Photosynthesis (FvCB Model)

from phytorch import photosynthesis as fvcb
import pandas as pd

# Load your A-Ci curve data
df = pd.read_csv('your_aci_data.csv')
lcd = fvcb.initLicordata(df, preprocess=True)

# Initialize and fit the FvCB model
model = fvcb.model(lcd, LightResp_type=2, TempResp_type=2)
result = fvcb.fit(model, learn_rate=0.08, maxiteration=20000)

# View fitted parameters
print(f"Vcmax25 = {result.params['Vcmax25']:.2f} μmol/m²/s")
print(f"Jmax25 = {result.params['Jmax25']:.2f} μmol/m²/s")

Stomatal Conductance

from phytorch import stomatalconductance as stomatal
import pandas as pd

# Load stomatal conductance data
df = pd.read_csv('your_gs_data.csv')
scd = stomatal.initscdata(df, preprocess=True)

# Fit Medlyn model
model = stomatal.model(scd, model_type='MED')
result = stomatal.fit(model, learn_rate=0.01, maxiteration=10000)

# View fitted parameters
print(f"g0 = {result.params['g0']:.4f} mol/m²/s")
print(f"g1 = {result.params['g1']:.2f}")

Package Structure

phytorch/
├── photosynthesis/      # FvCB photosynthesis models
├── stomatalconductance/ # Stomatal conductance models
├── leafoptics/          # PROSPECT leaf optical properties
├── leafhydraulics/      # Leaf hydraulics (under development)
├── data/                # Example datasets
└── util.py              # Utility functions

Documentation

Full documentation is available at https://phytorch.org

Citation

If you use PhyTorch in your research, please cite:

Lei, T., Rizzo, K. T., & Bailey, B. N. (2025). PhoTorch: A robust and generalized
biochemical photosynthesis model fitting package. (In preparation)

Credits

PhyTorch is an extension and reorganization of PhoTorch, developed by:

• Tong Lei
• Kyle T. Rizzo
• Brian N. Bailey

License

PhyTorch is licensed under the GNU General Public License v3.0. See LICENSE for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

For questions or issues, please open an issue on GitHub.